Random

Overview

The Random Node generates a random outcome, usually a number.

This Node can be set to three different Modes (Advanced, Expert, and Standard). Each of these Modes offers a different set of Attributes that are explained below.

Scope: Project, Scene, Function, Prefab.

Attributes

Each Mode has a different set of Attributes. The Modes are: Advanced, Expert, and Standard.

Advanced

Generator

This Mode allows the user to choose whether the random generator is deterministic or not, and for the deterministic case, the seed to use.

AttributeTypeDescription

Is Deterministic

Bool

Whether the random generator is deterministic or not.

Seed

Int (only available when Is Deterministic is set to true)

The Seed to use for the deterministic random generator.

Distribution

This Mode has a Drop-down menu from which the probability distribution used for the random generator can be chosen. Each option offers its own set of Attributes with the probability distribution parameters.

AttributeTypeDescription

Distribution

Drop-down

The probability distribution that the random generator will use.

Next, the Attributes for each probability distribution are described. For each probability distribution, the link to its corresponding Wikipedia entry is given.

Probability distribution of a random variable that can take two values: true, with probability p; and false, with probability 1-p. When this distribution is chosen, the outcome of the Node is a Boolean.

AttributeTypeDescription

Probability of 'true'

Float (between 0 and 1)

The probability that the outcome will be true.

Probability distribution of the number of successes in a sequence of independent experiments, each one with two possible outcomes: success and failure. The parameters for this probability distribution are the number of experiments and the probability of a successful outcome in each one.

AttributeTypeDescription

Data Type

Drop-down

Whether the outcome will be an Int or Byte.

Probability of 'true'

Float

The probability that the outcome of each trial is true.

Number of trials

Int

The number of independent experiments, each one with probability of success Probability of 'true'.

Symmetric probability distribution, with half its values less than the mean and half greater than the mean. The parameters are the mean, which equals the median and the mode, and the standard deviation.

AttributeTypeDescription

Mean

Float

The mean value of the distribution.

Standard deviation

Float

The standard deviation of the distribution.

Discrete probability distribution that expresses the probability of a given number of events occurring in a specified time period. Its parameter is the mean value.

AttributeTypeDescription

Data Type

Drop-down

Wheter the outcome will be an Int or Byte.

Mean

Float

The mean value of the distribution.

  • Uniform

Probability distribution in which all the values in an interval are equally likely to be drawn. It can either be continuous or discrete.

AttributeTypeDescription

Data Type

Drop-down

Whether an Int, Float, or Byte will be generated.

Minimum

Defined in the Data Type Attribute

The lower bound of the interval from which the random number will be extracted.

Maximum

Defined in the Data Type Attribute

The upper bound of the interval from which the random number will be extracted.

Expert

Generator

This Mode allows to choose from a list of several types of random generators.

AttributeTypeDescription

Generator

Drop-down

The type of random generator to use.

Seed

Int (not available for non_deterministic Generator)

The Seed to use for the random generator.

Distribution

This Mode has a Drop-down menu from which the probability distribution to be used for the random generator can be chosen. Each option offers its own set of Attributes with the probability distribution parameters.

AttributeTypeDescription

Distribution

Drop-down

The probability distribution that the random generator will use.

Next, the Attributes for each probability distribution are described. For each probability distribution, the link to its corresponding Wikipedia entry is given.

Probability distribution of a random variable that can take two values: true, with probability p; and false, with probability 1-p. When this distribution is chosen, the outcome of the Node is a Boolean.

AttributeTypeDescription

Probability of 'true'

Float (between 0 and 1)

The probability that the outcome will be true.

Probability distribution of the number of successes in a sequence of independent experiment, each one with two possible outcomes: success and failure. The parameters for this probability distribution are the number of experiments and the probability of a successful outcome in each one.

AttributeTypeDescription

Data Type

Drop-down

Whether the outcome will be an Int or Byte.

Probability of 'true'

Float

The probability that the outcome of each trial is true.

Number of trials

Int

The number of independent experiments performed, each one with probability of success Probability of 'true'.

Probability distribution that resembles a normal distribution but with a taller peak, whose tails decay slower. Its parameters are the location of the peak and the scale - the latter defines its width.

AttributeTypeDescription

Location

Float

Defines where the peak is.

Scale

Float

Half the width of the probability density function at half the maximum height.

Probability distribution of a sum of the squares of a number of independent normal random variables. The number of normal random variables is called the degrees of freedom of the Chi-squared distribution.

AttributeTypeDescription

Degrees of freedom

Float

Number of independent normal random variables that are summed.

Probability distribution of the time between events in a Poisson process. Its parameter is the rate at which the events in the Poisson process occur.

AttributeTypeDescription

Rate

Float

Rate at which the events in the Poisson process occur.

Limit distribution of properly normalized maxima of a sequence of independent and identically distributed random variables.

AttributeTypeDescription

Location

Float

Defines where the peak is.

Scale

Float

Defines how spread out the values are.

Ratio of two independent random variables with chi-squared distributions, each one divided by its corresponding number of degrees of freedom for scaling.

AttributeTypeDescription

Denominator Dof

Float

Degrees of freedom of the chi-squared random variable in the denominator.

Numerator DoF

Float

Degrees of freedom of the chi-squared random variable in the numerator.

Maximum entropy probability distribution for a random variable, whose mean is the product between the shape and scale, which are the two parameters of the Gamma distribution.

AttributeTypeDescription

Shape

Float

Modifies the shape of the probability distribution.

Scale

Float

Defines how spread out are the values.

The probability distribution of the number of experiments with a Bernoulli distribution needed to get one success.

AttributeTypeDescription

Data Type

Drop-down

Whether the output is an Int or Byte.

Probability of 'true'

Float (between 0 and 1)

The probability of success in the Bernoulli trials.

Probability distribution of a random variable whose logarithm has a normal distribution.

AttributeTypeDescription

Mean

Float

The mean value of the logarithm of the distribution.

Standard deviation

Float

The standard deviation of the logarithm of the distribution.

Probability distribution of the number of successes in a sequence of independent experiments, each with two possible outcomes: success and failure, before a specified non-random number of failures occur. The parameters for this probability distribution are the probability of a successful outcome in each experiment and the number of failures until the experiments stop.

AttributeTypeDescription

Data Type

Drop-down

Whether the outcome is an Int or Byte.

Probability of 'true'

Float (between 0 and 1)

The probability that the outcome of each trial is true.

Number of trials

Int

The number of failures to occur until the experiments stop.

Symmetric probability distribution, with half its values less than the mean and half greater than the mean. The parameters are the mean, which equals the median and the mode, and the standard deviation.

AttributeTypeDescription

Mean

Float

The mean value of the distribution.

Standard deviation

Float

The standard deviation of the distribution.

Discrete probability distribution that expresses the probability of a given number of events occurring in a specified time period. Its parameter is the mean value.

AttributeTypeDescription

Data Type

Drop-down

Wheter the outcome will be an Int or Byte.

Mean

Float

The mean value of the distribution.

Probability distribution that arises when estimating the mean of a normally-distributed statistical population with a small sample size and unknown standard deviation. Its parameter is the number of degrees of freedom, which is the number of observations taken from a normal distribution minus one.

AttributeTypeDescription

Degrees of freedom

Float

The number of observations taken from a normal distribution minus one. As it grows, the Student-t distribution approaches a normal distribution.

  • Uniform

Probability distribution in which all the values in an interval are equally likely to be drawn. It can either be continuous or discrete.

AttributeTypeDescription

Data Type

Drop-down

Whether an Int, Float, or Byte will be generated.

Minimum

Defined in the Data Type Attribute

The lower bound of the interval from which the random number will be extracted.

Maximum

Defined in the Data Type Attribute

The upper bound of the interval from which the random number will be extracted.

AttributeTypeDescription

Shape

Float

Defines the shape of the probability distribution.

Scale

Float

Defines how spread out the values of the probability distribution are.

Standard

This Mode only uses a uniform distribution. It can either be discrete or continuous.

Distribution

AttributeTypeDescription

Data Type

Drop-down

Whether an Int, Float, or Byte will be generated.

Minimum

Defined in the Data Type Attribute

The lower bound of the interval from which the random number will be extracted.

Maximum

Defined in the Data Type Attribute

The upper bound of the interval from which the random number will be extracted.

Inputs

InputTypeDescription

Pulse Input (►)

Pulse

A standard Input Pulse, to trigger the execution of the Node.

Outputs

OutputTypeDescription

Pulse Output (►)

Pulse

A standard Output Pulse, to move onto the next Node along the Logic Branch, once this Node has finished its execution.

Output

Depends on the Mode and Distribution

The random outcome that was generated.

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